With the rapid expansion of smart cities, Intelligent Transportation Systems (ITS) are assuming an increasingly pivotal role. Among the multitude of tasks within ITS, traffic state forecasting stands out. As city boundaries grow, traffic forecasting encounters scalability and network transmission challenges. This research contributes to traffic state forecasting within large-scale, massive Internet of Things (IoT) scenarios. By investigating an urban expressway managing architecture that employs LoRaWAN communication, a novel deep learning-based model named Time Alignment based Temporal-Graph Attention Network (TATGaN) is proposed. Using the temporal-graph attention mechanism, TATGaN is able to extract temporal-spatial information and predict traffic state accurately. Moreover, the time alignment block makes TATGaN capable of handling irregular sequences given by the asynchronous arrival of packets. Simulation results based on OSM data of a specific region within Abu Dhabi show that TATGaN outperforms existing baseline methods in prediction performance with lower error and the higher reliability. Furthermore, the performance evaluation demonstrates the suitability of TATGaN for large-scale traffic network scenarios, attributing its efficiency to the transmission schedule and parameter mechanisms in LoRaWAN networks.

LoRaWAN (Long Range Wide Area Network) has garnered significant attention within the Internet of Things (IoT) due to its ability to establish a wireless network with massive devices over long distances while minimizing energy consumption. Our previous work shows its suitability for Intelligent Transportation Systems (ITS) scenarios. However, the utilization of the Aloha MAC protocol presents a challenge for LoRaWAN as it grapples with the presence of compromised nodes. These nodes may engage in greedy behaviors, disregarding network regulations to enhance their own performance or acquire additional network resources, and are often difficult to detect. This research contributes to machine learning-based greedy behavior detection methods. After proposing several end-to-end (E2E) methods with different ML algorithms, EDLoG (Encoder-based detection method of LoRaWAN Greedy behaviors) is proposed. It is a greedy behavior detection method combining a Multilayer Perceptron (MLP) encoder network and a statistical abnormal detection algorithm. The performance evaluations are conducted using simulation data under different scenarios given by MELoNS, a Modular and Extendable Simulator for the LoRaWAN Network developed in our previous work. The results show that the proposed method gives a detection recall 15%-20% higher than the baseline method by keeping a high detection precision. Moreover, the proposed methods show high timing efficiency with a running time much smaller than LoRaWAN’s time scale, making the method easily deployed to a real LoRaWAN network.

With the rapid growth of smart cities, Intelligent Transportation Systems (ITS) are playing an increasingly important role. However, with the rapid expansion of city boundaries, ITS faces scalability and energy consumption challenges. The wide range and the massive number of nodes have become significant issues for network technology in ITS. This study proposes an urban expressway managing architecture using LoRaWAN and edge computing. The traffic network is divided into different sections. A LoRa WAN network is established for monitoring and controlling each section by exploiting its low-power, long-range characteristics. Moreover, an edge computing-based traffic state encoder model has been proposed to handle the large amount of data generated by the massive number of nodes. The architecture's procedure allocates tasks to LoRaWAN devices by exploiting their different computing capabilities. Simulation results on real maps of both Abu Dhabi and Beijing demonstrate the high performance and scalability of the architecture. Numerical results also show that the encoder model can effectively reduce network packet size by extracting data features.

LoRaWAN is a network technology that provides a long-range wireless network at low energy consumption. In order to save energy, it takes the pure Aloha MAC protocol and the duty-cycle limitation at both uplink and downlink on the MAC layer. Moreover, LoRaWAN also adapts the orthogonal parameters to avoid the collision. However, the star-topology synchronization and the complicated collision mechanism make quantitative model analysis difficult in LoRaWAN. This study modeled the Class-A device in the LoRaWAN protocol using Probabilistic Timed Automata (PTA). It is a mathematical model that presents the nondeterministic and probabilistic choice with time passing. Using the time representation in PTA, the transmission schedule of LoRaWAN's MAC layer of is modeled. Moreover, the full collision model is built in the PTA. Several properties are verified with the probabilistic model checker PRISM, and the quantitative properties are calculated under different cases.

LoRaWAN (Long Range Wide Area Network) is rapidly gaining attention in the Internet of Things (IoT) due to its ability to provide a long-range wireless network at low energy consumption. However, with the Aloha MAC protocol, LoRaWAN faces the challenge of malicious behaviors from compromised nodes. Compromised nodes can take greedy behaviors by breaking network rules to improve their performance or obtain more network resources. This study proposes and investigates different greedy behaviors of infected nodes in LoRaWAN on the MAC layer. A straightforward double judgment detection method is proposed. The simulation results show that although the MAC layer of LoRaWAN is robust and resilient against greedy behavior by design, it can still be negatively affected by high-intensity greedy behavior. Moreover, the simulation results also show the high performance of the proposed detection method in different greedy behavior scenarios.

LoRaWAN (LOng RAnge radio Wide Area Network) is rapidly gaining attention with its capacity for a large devices number, long-range, and low power consumption. Many research works are carried out to evaluate or improve LoRaWAN’s performance in different application scenarios. However, because of a large number of connected devices and a complex environment, it is sometimes impractical to validate the studies with real test-bed or analytical methods. Therefore, the development of accurate network simulators is needed. More-over, extendability and flexibility are invaluable for a simulator to evaluate network performance in different scenarios and studies under a unified environment. To that end, this work presents MELoNS, a modular, extendable LoRaWAN simulator. By representing complete LoRaWAN transmission procedures at the different layers, MELoNS is suitable for the simulation of LoRaWAN in any scenario. MELoNS is lightweight and modular. The independence of network components gives MELoNS a high degree of freedom. Making MELoNS easy to implement and extend for different studies in the same environment. An example application of an intelligent traffic monitoring network is given to show the features of MeLONS.

LoRaWAN attracted lots of attention with its capacity for large device numbers, long-range, and low-power consumption. In order to simplify the transmission procedure, a pure Aloha protocol is implemented into its MAC layer. However, as the number of connected devices to the base station increases, the devices’ transmission parameters allocation becomes a vital issue related to network performance. This research contributes to the decentralized dynamic spreading factor (SF) allocation strategies during transmission by proposing a score table-based evaluation and parameters surfing (STEPS) approach. STEPS is a reinforcement learning-based method that evaluates and changes the parameters based on probability and score tables. It provides a nondeterministic parameter selection method by updating the table while transmitting. Some variants of STEPS with different algorithms are proposed. Moreover, an estimation-based initialization is proposed to improve learning performance. Simulations and statistical tests are carried out with MULANE, a lightweight LoRaWAN Simulator developed in our previous work. The results show that the estimation has a high confidence level. Compared with the baseline methods, the proposed methods reduce energy consumption by 24%–27% in different numbers of nodes. For bi-directional transmission, the proposed methods increase the 18% network throughput in a small number of nodes and 33% in a large number of nodes. Moreover, the proposed methods provide a framework of decentralized parameter allocation, which gives the extendability of this work.

Sun-induced chlorophyll fluorescence (SIF) is a promising remote sensing signal for early stress detection due to its close link with photosynthesis. Canopy SIF signals are controlled by leaf physiology, canopy structure, radiation intensity and sun-observer geometry. Variations in SIF observations are affected by variations in these controlling factors besides water stress. Mitigating the interference of non-drought factors on the variations in canopy SIF to accurately evaluate drought degree is still challenging. In this study, we explore the response of apparent SIF yield (SIFy) to progressive drought in maize. With experimental evidence, we show that the difference between noon and morning SIFy was a better indicator of drought than mono-temporal SIFy measurements. We proposed the noon-to-morning ratio (NMR) to characterize diurnal dynamics and assess the severity of drought. The results show that midday measurements of SIFy were the most affected by water stress, and morning measurements were the least. The NMR of SIFy successfully revealed water stress by tracking the timing of the transition from light-limited to water-limited conditions of SIF within a day. Hence, the NMRs of SIFy were considerably more sensitive to drought than their mono-temporal values, and traditional vegetation indices, especially during the early phase of drought. This demonstrates that the use of multi-temporal or diurnal SIF measurements is more reliable than mono-temporal observations for stress detection.

LoRaWAN is one of the network technologies that provide a long-range wireless network at low energy consumption. However, the pure Aloha MAC protocol and the duty-cycle limitation at both end devices and gateway make LoRaWAN very sensitive to malicious behaviors in the MAC layer. Moreover, this kind of sensitivity makes the false-positives problem challenging for malicious behavior detection with simple threshold methods. This study investigates two malicious behaviors - greedy and attack on the MAC layer. Furthermore, by combining the threshold method with a Local Outlier Factor (LOF) model in machine learning, LoRaLOFT is proposed. It is a centralized malicious node detection method. Analytical results show that the proposed method gives high detection accuracy while significantly reducing the false-positive rate in both behaviors.

As one of the technologies in the wide-area network category, LoRaWAN provides a wireless network with a large capacity of end devices (ED) in long-range. With a pure Aloha protocol implemented into its MAC layer, LoRaWAN can reduce its power consumption. Besides, some orthogonal transmission parameters give LoRaWAN capability to avoid collision and packet loss. Thus, allocating transmission parameters to increase the network performance becomes a challenging issue for LoRaWAN. Some dynamic Spreading Factor (SF) allocation strategies are studied in this paper. A distributed Markov Decision Process (MDP) model is constructed for the uplink transmission of the class-A device in LoRaWAN. The model is also solved and implemented to the algorithms for the initial strategy prediction. Analytical results show that the MDP model increases the performance of the studied algorithms on the transmission of the packet.

LoRaWAN (LOng RAnge radio Wide Area Network) belongs to the LPWAN (Low Power Wide Area Network) category, it aims to provide a wide area, long range and low power consumption communication network solution. However, with the large number of connected devices to the base-station and a pure Aloha MAC protocol, the packet lost and collisions become an important issue in the network related to the transmission parameters of the devices. For this issue, STEPS is proposed in this study. The goal of the proposed method is to establish a score table based evaluation and parameters surfing approach. STEPS provides an approach of evaluating and changing the parameters based on probability and score table when transmission failure appears. It can also update the table while transmitting. STEPS is compatible with pure Aloha, and doesn't need any change on the MAC protocol and is easy to be implemented. In this study, the spreading factor is chosen as the parameter to establish the table, that will be evaluated and to be surfed. Simulation results show that STEPS provides a remarkable improvement on bi-directional transmission of the packet and a capability of decreasing the packet lost and collisions even with higher data rate than classical LoRaWAN scenario.

As one of the LPWAN categories, LoRaWAN attracts great attention on IoT and M2M communication. It offers a comprehensive area communication network solution with a low data rate and low power consumption. Many research works on medium scheduling or resource allocation have been proposed to improve network performance. However, with many devices in the network, it is sometimes too difficult to deploy a test-bed to evaluate the network performance in the industry. However, different studies often use different simulators. Thus, it is also challenging to study the impact of different algorithms on the network by simulation. Thus, an extendable simulator becomes necessary for both industrial networks deploying and academic study. In this paper, we developed a lightweight, extendable simulator exclusive to LoRaWAN named MULANE. We also offered a GUI is to simplify the simulation process. Moreover, the simulator is agent-oriented, making the research works implementation easier by creating new agents,

Abstract In China, Type 2 diabetes mellitus (T2DM) is increasingly affecting people's health. Although many risk factors related to T2DM have been researched, the association between social relationships and risk management of T2DM in China has not been fully researched. Therefore, we obtained 2,969 valid cases from the National Chinese Medicine Clinical Research Base‐Key Disease of Diabetes Mellitus Study to evaluate the role of social relationships in the risk management of T2DM. We first establish an indicators system of social relationship factors and then propose a comprehensive method that integrates subjective (analytical network process) and objective (entropy weight method) evaluations to rank the importance of the 17 social relationship factors that were the most important and commonly used. The results suggest that different social relationship factors have different effects on the risk management of T2DM. Patients and health workers should pay more attention to the high‐benefit factors and thus improve the efficiency of the risk management of T2DM. These findings provided theoretical support for patients and health workers by developing the positive effects of social relationships in improving the risk management of T2DM to the fullest degree.

Spatial multiple access holds the promise to boost the capacity of wireless networks when an access point has multiple antennas. Due to the asynchronous and uncontrolled nature of wireless LANs, conventional MIMO technology does not work efficiently when concurrent transmissions from multiple stations are uncoordinated. In this paper, we present the design and implementation of a crosslayer system, called SAM, that addresses the challenges of enabling spatial multiple access for multiple devices in a random access network like WLAN. SAM uses a chain-decoding technique to reliably recover the channel parameters for each device, and iteratively decode concurrent frames with misaligned symbol timings and frequency offsets. We propose a new MAC protocol, called CCMA, to enable concurrent transmissions by different mobile stations while remaining backward compatible with 802.11. Finally, we implement the PHY and MAC layer of SAM using the Sora high-performance software radio platform. Our evaluation results under real wireless conditions show that SAM can improve network uplink throughput by 70% with two antennas over 802.11.

Much of the visualization research has focused on improving the rendering quality and speed, and enhancing the perceptibility of features in the data. Recently, significant emphasis has been placed on focus+context (F+C) techniques (e.g., fisheye views and magnification lens) for data exploration in addition to viewing transformation and hierarchical navigation. However, most of the existing data exploration techniques rely on the manipulation of viewing attributes of the rendering system or optical attributes of the data objects, with users being passive viewers. In this paper, we propose a more active approach to data exploration, which attempts to mimic how we would explore data if we were able to hold it and interact with it in our hands. This involves allowing the users to physically or actively manipulate the geometry of a data object. While this approach has been traditionally used in applications, such as surgical simulation, where the original geometry of the data objects is well understood by the users, there are several challenges when this approach is generalized for applications, such as flow and information visualization, where there is no common perception as to the normal or natural geometry of a data object. We introduce a taxonomy and a set of transformations especially for illustrative deformation of general data exploration. We present combined geometric or optical illustration operators for focus+context visualization, and examine the best means for preventing the deformed context from being misperceived. We demonstrated the feasibility of this generalization with examples of flow, information and video visualization.